Abstract
The parameters tuning of event generators is a research topic characterized by complex choices: the generator response to parameter variations is difficult to obtain on a theoretical basis, and numerical methods are hardly tractable due to the long computational times required by generators. Event generator tuning has been tackled by parametrization-based techniques, with the most successful one being a polynomial parametrization. In this work, an implementation of tuning procedures based on artificial neural networks is proposed. The implementation was tested with closure testing and experimental measurements from the ATLAS experiment at the Large Hadron Collider. Program summaryProgram Title:MCNNTUNESCPC Library link to program files:https://doi.org/10.17632/dmkydsxgd3.1Developer’s repository link:https://github.com/N3PDF/mcnntunesLicensing provisions: GPLv3Programming language:PythonNature of problem: Shower Monte Carlo generators introduce many parameters that must be tuned to reproduce the experimental measurements. The dependence of the generator output on these parameters is difficult to obtain on a theoretical basis.Solution method: Implementation of a tuning method using supervised machine learning algorithms based on neural networks, which are universal approximators.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.